The present invention relates to an agricultural harvesting machine.
For the operator of a harvesting machine, it is imperative that the parameters of the machine components be set correctly in order to ensure that a harvesting process is attained that reaches a desired harvesting goal to be attained at the end of the harvesting process chain. As a result, it is now common when harvesting grass or corn for silage, for instance, for the operator to set operating parameters, e.g. the length of cut, before and while working a field. The operator relies largely on experience when setting the operating parameters. Since other vehicles are used in the harvesting process chain in addition to the harvesting machines, e.g. hauling vehicles and compacting vehicles in the silo, very complex interrelationships exist between the vehicles involved in the harvesting process and their influence on the desired harvesting goal to be attained at the end of the harvesting process chain, such as the desired level of compaction of crop material in a silo, thereby making it difficult even for experienced operators to estimate whether the operating parameters he selected will actually result in the desired harvesting goal being attained. In particular, the comparison that is used, i.e. a comparison of an actual value of individual operating parameters with a setpoint value (=ideal value) for the particular operating parameter, is not very effective in predicting whether or not a desired harvesting goal will be attained at the end of the harvesting process chain.
Several possibilities for solving this problem have been made known in the related art; the possibilities are designed to assist the operator of the agricultural harvesting machine in selecting the optimal operating parameters. For example, EP 1297 733 A1 makes known a method for determining harvesting machine settings, in the case of which the harvesting machine is acted upon during operation by a nearly consistent quantity of crop material, then the working result is recorded after a certain time delay and is stored together with the associated operating parameters of the working units. The operator may now change an operating parameter of a working unit, the harvesting machine restarts operation, and the process—described above—of recording the working results and storing them together with the associated operating parameters is repeated. The two recorded working results are now compared with each other, and the operating parameters of the better working result are used to set up the working units. The disadvantage of this method is that, due to the time delay between the particular start of the harvesting operation and the instant when the working result is attained, a great deal of time is required to optimize the operating parameters.
It is also disadvantageous that the operator must repeatedly try to attain a nearly constant crop material throughput rate in order to ensure that the working results obtained using different operating parameters may be compared with one another. However, it is not possible to make a reliable prognosis as to whether a desired harvesting goal is attainable. This requires a great deal of experience and ability on the part of the operator, and it requires a great deal of time. A further example of entering settings for a harvesting machine is disclosed in U.S. Pat. No. 5,586,033, in which a control system trains a neural network model of the harvesting machine with data. The model is then used to select the settings for the harvesting machine. However, neural networks having large capacity require a level of computational ability that is practically unaffordable. Given the current status of development of neural network techniques, large neural networks have limited practical utility in harvesting machine applications.